Comparison
Fine-tuning vs Synthetic Data
Fine-tuning and Synthetic Data are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Fine-tuning
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples.
Fine-tuning Llama 3 on medical Q&A for a clinical assistant.
When you would reach for Synthetic Data
Synthetic Data comes up when the question is fundamentally about training.
Phi-3 trained heavily on textbook-quality synthetic data.
Frequently asked
What is the difference between Fine-tuning and Synthetic Data?
Fine-tuning: Fine-tuning continues training a pretrained model on a smaller, task-specific dataset, adjusting its weights to specialize behavior or knowledge. Synthetic Data: Synthetic data is training data produced by a model — instructions distilled from GPT-4, code generated and filtered by tests, reasoning traces sampled from a stronger model — rather than handwritten by humans.
When should I use Fine-tuning vs Synthetic Data?
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples. Synthetic Data applies when you are focused on training.
Are Fine-tuning and Synthetic Data the same thing?
No. Fine-tuning is training; Synthetic Data is training. They are related but address different parts of the AI stack.